Welcome Address and Opening Remarks

Keynote by Sudhakar Yalamanchili, Georgia Institute of Technology, USA: Scaling Resource Compositions in a Flatter World

Technical Session 1: GPU-based computing

Ray Bittner, Erik Ruf: Direct GPU/FPGA Communication Via PCI Express

Parallel processing has hit mainstream computing in the form of CPUs, GPUs and FPGAs. While explorations proceed with all three platforms individually and with the CPU-GPU pair, little exploration has been performed with the synergy of GPU-FPGA. This is due in part to the cumbersome nature of communication between the two. This paper presents a mechanism for direct GPU-FPGA communication and characterizes its performance in a full hardware implementation.

Brad Suchoski, Caleb Severn, Manu Shantharam and Padma Raghavan: Adapting Sparse Triangular Solution to GPUs

High performance computing systems are increasingly incorporating hybrid CPU/GPU nodes to accelerate the rate at which floating point calculations can be performed for scientific applications. Currently, a key challenge is adapting scientific applications to such systems when the underlying computations are sparse, such as sparse linear solvers for the simulation of partial differential equation models using semiimplicit methods. Now, a key bottleneck is sparse triangular solution for solvers such as preconditioned conjugate gradients (PCG). We show that sparse triangular solution can be effectively mapped to GPUs by extracting very large degrees of fine grained parallelism using graph coloring. We develop simple performance models to predict these effects at intersection of the data and hardware attributes and we evaluate our scheme on a Nvidia Tesla M2090 GPU relative to the level set scheme developed at NVIDIA. Our results indicate that our approach significantly enhances the available fine-grained parallelism to speed-up execution time compared to the NVIDIA scheme, by a factor with a geometric mean of 5.41 on a single GPU, with speedups as high as 63 in some cases.

Presentation

Pedro Valero-Lara: MRF satellite image classification on GPU

One of the stages of the analysis of satellite images is given by a classification based on the Markov Random Fields (MRF) method. It is possible to find in literature several packages to carry out this analysis, and of course the classification tasks. One of them is the Orfeo ToolBox (OTB). The analysis of satellite images is an expensive computational task requiring real time execution or automatization. In order to reduce the execution time spent on the analysis of satellite images, parallelism techniques can be used. Currently, Graphics Processing Units (GPUs) are becoming a good choice to reduce the execution time of several applications at a low cost. In this paper, the author presents a GPU-based classification using MRF from the sequential algorithm that appears in the OTB package. The experimental results show a spectacular reduction of the execution time for the GPU-based algorithm, up to 225 times faster than the sequential algorithm included in the OTB package. Moreover, this result is also observed in the total power consumption, which is reduced by a significant amount.

Presentation

Keynote by Joel Emer, Intel, USA:
Scaling the Von-Neumann Wall with Reconfigurable Computing

Technical Session 2: FPGA-based computing

Yamuna Rajasekhar and Ron Sass: Architecture and Applications for an All-FPGA Parallel Computer

Technical Session 3: Low-power based computing